使用基于水平可见性图的网络对跨尺度流量可预测性的见解
Insights On Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks
摘要：
河川径流是流域边界内水运动在空间和时间上的综合动力过程。作者描述了与来自美国地质调查局（USGS）在爱荷华州的71个流量测量站的流量时间序列数据相关的动态。他们采用了一种新的方法，称为可见性图（VG）。它利用时间序列映射到复杂网络的概念来研究动力系统的时间演化行为。作者专注于VG算法的一个简单变体，称为水平可见性图（HVG）。通过提取HVG导出网络的两个关键信息：度分布特征指数λ和全局聚类系数GC，实现了对动态过程的跟踪，从而实现了对径流过程的可预测性。利用这两种测度来判别径流过程的起源是随机过程还是混沌过程。它们表明，流量动态的表征是敏感的数据属性。通过系统、全面的分析，说明了径流动态特性对径流时间序列的归一化和时间尺度的敏感性。在每日尺度上，分析中使用的所有站点的径流量显示出具有强烈空间尺度（流域大小）依赖性的随机性。这对径流和洪水的可预测性产生了影响。作者证明了随着平均时间尺度的增加，动力学从潜在的混沌过程过渡到随机相关过程。最后，λ和GC的时间趋势在所分析的站点总数的约40%处具有统计学显著性。将这一趋势归因于气候变化或土地使用等因素，需要进一步研究。
英文摘要：
Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, λ of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of λ and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research.
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